Institute for Systems Research Technical Reports

Permanent URI for this collectionhttp://hdl.handle.net/1903/4376

This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.

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Author: search.filters.author.Berenstein, Carlos A.Subject: search.filters.subject.computational complexity

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Now showing 1 - 4 of 4
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    Residue Calculus and Effective Nullstellensatz
    (1996) Berenstein, Carlos A.; Yger, A.; ISR
    We provide new tools to compute multidimensional residues for rational functions, even over fields of positive characteristic. As a corollary one obtains solutions of the Betout equation for polynomials over a ring with a site that have almost optimal estimates for degree and size.
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    Green Currents and Analytic Continuation
    (1995) Berenstein, Carlos A.; Yger, A.; ISR
    Using the construction of residue currents via analytic continuation we give explicit formulas for green currents which have singularities along algebraic varieties in projective space, as long as they are complete intersections.
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    Resudus, Courants residuels et Courants de Green
    (1994) Berenstein, Carlos A.; Gay, Roger; Yger, A.; ISR
    Some explicit formulas are provided in order to solve division problems in commutative algebra or questions related to intersection theory; it is shown here how the ides of analytic continuation of distributions leads to some explicit solution for Green equation for algebraic complete intersections in the projective space.
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    Exact, Recursive, Inference of Event Space Probability Law for Discrete Random Sets with Applications
    (1991) Sidiropoulos, N.; Baras, John S.; Berenstein, Carlos A.; ISR
    In this paper we extend Choquet's result to obtain a recursive procedure for the computation of the underlying event-space probability law for Discrete Random Sets, based on Choquet's capacity functional. This is an important result, because it paves the way for the solution of statistical inference problems for Discrete Random Sets. As an example, we consider the Discrete Boolean Random Set with Radial Convex Primary Grains model, compute its capacity functional, and use our procedure to obtain a recursive solution to the problem of M-ary MAP hypothesis testing for the given model. The same procedure can be applied to the problem of ML model fitting. Various important probability functionals are computed in the process of obtaining the above results.